How do health information seeking and scanning motivate older adults to use patient portals? A mediation analysis based on the technology acceptance model
Restricted accessResearch articleFirst published online 2026
How do health information seeking and scanning motivate older adults to use patient portals? A mediation analysis based on the technology acceptance model
Patient portals are recognized as a promising platform to support patient engagement; yet, their adoption remains low, especially among older adults. Drawing upon the Technology Acceptance Model, this study developed and tested a mediation model that links health information acquisition (seeking and scanning) to patient portal use via perceived ease of use (PEOU) and perceived usefulness (PU; N = 732). Results showed that both seeking and scanning of health information are positively associated with portal use among the elderly with chronic conditions, but in different ways. Purposeful seeking enhances both PEOU and PU, whereas incidental scanning only affects PU through PEOU. Furthermore, PU directly predicted portal use, while PEOU exerted its influence indirectly via PU. Findings highlight distinct cognitive pathways for different information acquisition behaviors, suggesting that interventions should encourage active seeking and prioritize functional usefulness to improve eHealth adoption in aging populations.
AliMAAlamKTaylorB, et al. (2021) Examining the determinants of eHealth usage among elderly people with disability: The moderating role of behavioural aspects. International Journal of Medical Informatics149: 104411.
2.
BigsbyEHovickSR (2018) Understanding associations between information seeking and scanning and health risk behaviors: An early test of the structural influence model. Health Communication33(3): 315–325.
3.
BorahPSuYXiaoX, et al. (2022) Incidental news exposure and COVID-19 misperceptions: A moderated-mediation model. Computers in Human Behavior129: 107173.
4.
Burton-JonesAHubonaGS (2006) The mediation of external variables in the technology acceptance model. Information Management43(6): 706–717.
5.
China Internet Network Information Center (2025) Statistical Report on Internet Development in China. China Internet Network Information Center.
6.
ClarkeMALydenERMaJ, et al. (2021) Sociodemographic differences and factors affecting patient portal utilization. Journal of Racial and Ethnic Health Disparities8: 879–891.
7.
DavisFD (1989) Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly13: 319–340.
8.
El-YafouriRKliebLSabatierV (2022) Psychological, social and technical factors influencing electronic medical records systems adoption by United States physicians: A systematic model. Health Research Policy and Systems20(1): 48.
9.
GundersonJMitchellDReidK, et al. (2021) COVID-19 information-seeking and prevention behaviors in Florida, April 2020. Preventing Chronic Disease18: E17. doi: 10.5888/pcd18.200575.
10.
HardgraveBCDavisFDRiemenschneiderCK (2003) Investigating determinants of software developers' intentions to follow methodologies. Journal of Management Information Systems20(1): 123–151.
11.
HayesAF (2017) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford publications.
12.
Healthy China Action Plan (2019–2030) National Health Commission of the PRC.
13.
HoldenRJKarshB-T (2010) The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics43(1): 159–172.
14.
HongYAZhouZ (2018) A profile of eHealth behaviors in China: Results from a national survey show a low of usage and significant digital divide. Frontiers in Public Health6: 274.
15.
HornikRParvantaSMelloS, et al. (2013) Effects of scanning (routine health information exposure) on cancer screening and prevention behaviors in the general population. Journal of Health Communication18(12): 1422–1435.
16.
HsiehP-J (2015) Physicians’ acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk. International Journal of Medical Informatics84(1): 1–14.
17.
KahlorLDunwoodySGriffinRJ (2004) Predicting knowledge complexity in the wake of an environmental risk. Science Communication26(1): 5–30.
18.
KellyBHornikRRomantanA, et al. (2010) Cancer information scanning and seeking in the general population. Journal of Health Communication15(7): 734–753.
19.
KovnerCTMezeyMHarringtonC (2002) Who cares for older adults? Workforce implications of an aging society. Health Affairs21(5): 78–89.
20.
KuhlthauCCHeinströmJToddRJ (2008) The ‘information search process’ revisited: Is the model still useful. Information research13(4): 13–14.
21.
LazardAJWatkinsIMackertMS, et al. (2016) Design simplicity influences patient portal use: The role of aesthetic evaluations for technology acceptance. Journal of the American Medical Informatics Association23(e1): e157–e161.
22.
LeeJKKimE (2017) Incidental exposure to news: Predictors in the social media setting and effects on information gain online. Computers in Human Behavior75: 1008–1015.
23.
LewisNAndrewsEAKeefeD, et al. (2024) A meta-analytical review of the relationship, antecedents, and consequences of information seeking and information scanning. Human Communication Research50(4): 468–479.
24.
LiuMYangYSunY (2019) Exploring health information sharing behavior among Chinese older adults: A social support perspective. Health Communication34(14): 1824–1832.
25.
LiuPLZhaoXYeJF (2022) The effects of the use of patient-accessible electronic health record portals on cancer survivors’ health outcomes: Cross-sectional survey study. Journal of Medical Internet Research24(10): 11.
26.
LiWChangL (2024) Aging in cyberspace: Exploring health information acquisition among older WeChat users. Global Media and China9(3): 279–302.
27.
Li YingWY (2015) An Analysis of the Demands of Elderly Audiences for Health and Wellness Television Programs in the New Media Era. Zhengzhou University of Light Industry (Social Science).
28.
LongoDR (2005) Understanding health information, communication, and information seeking of patients and consumers: A comprehensive and integrated model. Health Expectations8(3): 189–194.
29.
MandhanaDMGlowackiEMSunM-C (2021) Promoting patient portals: An application of social cognitive theory to post-adoption patient portal use. Health Communication36(14): 1990–2001.
30.
MarengoniAAnglemanSMelisR, et al. (2011) Aging with multimorbidity: A systematic review of the literature. Ageing Research Reviews10(4): 430–439.
31.
MaYLuoM (2024) Older people’s intention to use medical apps during the COVID-19 pandemic in China: An application of the unified theory of acceptance and use of technology (UTAUT) model and the technology of acceptance model (TAM). Ageing and Society44(7): 1515–1532.
32.
McKenziePJ (2003) A model of information practices in accounts of everyday-life information seeking. Journal of Documentation59(1): 19–40.
33.
NanzAMatthesJ (2022) Seeing political information online incidentally. Effects of first- and-and second-level incidental exposure on democratic outcomes. 133: 107285.
34.
National Bureau of Statistics of China (2021) Seventh National Population Census. National Bureau of Statistics of China.
35.
NiederdeppeJHornikRCKellyBJ, et al. (2007) Examining the dimensions of cancer-related information seeking and scanning behavior. Health Communication22(2): 153–167.
36.
OhSAhnJKimB (2003) Adoption of broadband Internet in Korea: The role of experience in building attitudes. Journal of Information Technology18(4): 267–280.
37.
Peña-Y-LilloM (2024) From information seeking and scanning to the practice of healthy habits: A longitudinal test of the integrative model of behavioral prediction in the context of fruit and vegetable consumption. Journal of Health Communication29(4): 284–293.
38.
PowellKR (2017) Patient-perceived facilitators of and barriers to electronic portal use: A systematic review. Computers Informatics Nursing35(11): 565–573.
39.
RuppelEK (2016) Scanning health information sources: Applying and extending the comprehensive model of information seeking. Journal of Health Communication21(2): 208–216.
40.
ScheerderAvan DeursenAvan DijkJ (2017) Determinants of internet skills, uses and outcomes. A systematic review of the second- and third-level digital divide. Telematics and Informatics34(8): 1607–1624.
41.
ShimMKellyBHornikR (2006) Cancer information scanning and seeking behavior is associated with knowledge, lifestyle choices, and screening. Journal of Health Communication11(S1): 157–172.
42.
SinghSPolavarapuMArseneC (2023) Changes in patient portal adoption due to the emergence of COVID-19 pandemic. Informatics for Health and Social Care48(2): 125–138.
43.
SlaterMD (1997) Persuasion processes across receiver goals and message genres. Communication Theory7(2): 125–148.
44.
SunJGuoYWangX, et al. (2016) mHealth for aging China: Opportunities and challenges. Aging and Disease7(1): 53–67.
45.
SwobodaCMDePuccioMJFareedN, et al. (2021) Patient portals: Useful for whom and for what? A cross-sectional analysis of national survey data. Applied Clinical Informatics12(3): 573–581.
46.
TrinhMHattakitjamroenVRogersWA (2024) Understanding older adults’ motivations to use digital health portals. Human Factors in Healthcare6: 100079.
47.
VallacherRRWegnerDM (1987) What do people think they're doing? Action identification and human behavior. Psychological Review94(1): 3–15.
48.
VelverthiNPrybutokVHongL (2024) An emerging adults’ patient portal behavioral model: integrating perceived risk theory, technology acceptance model, and personal innovativeness. International Journal of Human–Computer Interaction40(20): 6428–6441.
49.
VenkateshVBalaH (2008) Technology acceptance model 3 and a research agenda on interventions. Decision Sciences39(2): 273–315.
50.
VenkateshVDavisFD (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science46(2): 186–204.
51.
VenkateshVMorrisMGDavisGB, et al. (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27: 425–478.
52.
WangWZhangH (2023) Behavior patterns and influencing factors: Health information acquisition behavior of Chinese senior adults on WeChat. Heliyon9(6): e16431.
53.
WatsonKBWiltzJLNhimK, et al. (2025) Trends in multiple chronic conditions among US adults, by life stage, behavioral risk factor surveillance system, 2013-2023. Preventing Chronic Disease22: E15.
54.
WilsonJHeinschMBettsD, et al. (2021) Barriers and facilitators to the use of e-health by older adults: A scoping review. BMC Public Health21(1): 12.
55.
World Health Organization (2007) Global Age-Friendly Cities: A Guide. World Health Organization.
56.
WuQNgienAJiangS (2024) Descriptive norms and eHealth use among older adults: A cross-country comparative study. Health Communication39(12): 2971–2982.
57.
Xinhua News Agency (2026) Facing Hundreds of Millions With Chronic Diseases: How China Fights a Long Battle for Health. Xinhua News Agency.
58.
XuLZhangLDuanY, et al. (2026) Promoting support for tobacco control: The roles of online and offline health information seeking. Digital Health12: 20552076261431847.
59.
ZhangLYeJFZhaoX (2025c) “I saw it incidentally but frequently”: Exploring the effects of online health information scanning on lung cancer screening behaviors among Chinese smokers. Health Communication40(2): 345–356.
60.
ZhangLLiuMYouF, et al. (2024) How Do Health Information Scanning and seeking influence excessive alcohol drinking among Chinese hypertensive drinkers? A moderated mediation analysis. Health Communication40: 1615–1625.
61.
ZhangLLiuTXuL, et al. (2025a) How to effectively use online medical records: A moderated mediation analysis of patient-centered communication and self-efficacy in mitigating anxiety among patients with chronic diseases. Journal of Health Psychology. Epub ahead of print 13591053251383311.
62.
ZhangLQinYLiP (2020) Media complementarity and health information acquisition: A cross-sectional analysis of the 2017 HINTS-China survey. Journal of Health Communication25(4): 291–300.
63.
ZhangLXieQQiuS, et al. (2025b) Willing or reluctant to share health data? A moderated mediation analysis of wearable device usage and data-sharing intentions among older adults. Digital Health11: 20552076251337948.
64.
ZhaoXLiDMLaiZZ, et al. (2024) Percentage coefficient (bp)—Effect size analysis (Theory Paper 1). arXiv preprint, arXiv:2404.19495.
65.
ZhaoYCZhaoMSongS (2022) Online health information seeking behaviors among older adults: systematic scoping review. Journal of Medical Internet Research24(2): e34790.
66.
ZhuangJCobbC (2022) COVID-19 vaccine-related information seeking and scanning: A test of mediators between information acquisition and vaccination intention among unvaccinated Black Americans. Journal of Health Communication27(6): 353–361.
